TY - GEN
T1 - On the Complexity of Traffic Traces and Implications
AU - Avin, Chen
AU - Ghobadi, Manya
AU - Griner, Chen
AU - Schmid, Stefan
N1 - Publisher Copyright:
© 2019 Owner/Author.
PY - 2020/6/8
Y1 - 2020/6/8
N2 - This paper presents a systematic approach to identify and quantify the types of structures featured by packet traces in communication networks. Our approach leverages an information-theoretic methodology, based on iterative randomization and compression of the packet trace, which allows us to systematically remove and measure dimensions of structure in the trace. In particular, we introduce the notion of trace complexity which approximates the entropy rate of a packet trace. Considering several real-world traces, we show that trace complexity can provide unique insights into the characteristics of various applications. Based on our approach, we also propose a traffic generator model able to produce a synthetic trace that matches the complexity levels of its corresponding real-world trace. Using a case study in the context of datacenters, we show that insights into the structure of packet traces can lead to improved demand-aware network designs: datacenter topologies that are optimized for specific traffic patterns.
AB - This paper presents a systematic approach to identify and quantify the types of structures featured by packet traces in communication networks. Our approach leverages an information-theoretic methodology, based on iterative randomization and compression of the packet trace, which allows us to systematically remove and measure dimensions of structure in the trace. In particular, we introduce the notion of trace complexity which approximates the entropy rate of a packet trace. Considering several real-world traces, we show that trace complexity can provide unique insights into the characteristics of various applications. Based on our approach, we also propose a traffic generator model able to produce a synthetic trace that matches the complexity levels of its corresponding real-world trace. Using a case study in the context of datacenters, we show that insights into the structure of packet traces can lead to improved demand-aware network designs: datacenter topologies that are optimized for specific traffic patterns.
KW - complexity map
KW - compress
KW - data centers
KW - entropy rate
KW - self-adjusting networks
KW - trace complexity
UR - https://www.scopus.com/pages/publications/85087000337
U2 - 10.1145/3393691.3394205
DO - 10.1145/3393691.3394205
M3 - Conference contribution
AN - SCOPUS:85087000337
T3 - SIGMETRICS Performance 2020 - Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
SP - 47
EP - 48
BT - SIGMETRICS Performance 2020 - Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
PB - Association for Computing Machinery, Inc
T2 - 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2020
Y2 - 8 June 2020 through 12 June 2020
ER -